Potential global distribution of Setaria italica, an important species for dryland agriculture in the context of climate change

Setaria italica (S. italica, Linnaeus, 1753) is a drought-resistant, barren-tolerant, and widely adapted C-4 crop that plays a vital role in maintaining agricultural and economic stability in arid and barren regions of the world. However, the potential habitat of S. italica under current and future climate scenarios remains to be explored. Predicting the potential global geographic distribution of S. italica and clarifying its ecological requirements can help promote sustainable agriculture, which is crucial for addressing the global food crisis. In this study, we predicted the potential global geographic distribution of S. italica based on 3,154 global distribution records using the Maxent model and ArcGIS software. We assessed the constraints on its potential distribution based on the contribution of environmental factors variables. The predictive accuracy of the Maxent model was evaluated using AUC values, TSS values, and Kappa statistics, respectively. The results showed that the Maxent model had a high prediction accuracy, and the simulation results were also reliable; the total suitable habitats of S. italica is 5.54×107 km2, which mainly included the United States (North America), Brazil (South America), Australia (Oceania), China, India (Asia), and the Russian Federation (Europe). The most suitable habitat of S. italica was 0.52×107 km2, accounting for 9.44% of the total areas, mainly in the United States, India, the Russian Federation, and China. Soil and precipitation (driest monthly precipitation, hottest seasonal precipitation) are the most critical factors limiting the potential distribution of S. italica. Compared with the modern potential distribution, we predict that the four future climate change scenarios will result in varying reductions in the possible geographic ranges of S. italica. Overall, climate change may significantly affect the global distribution of S. italica, altering its worldwide production and trade patterns.

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Introduction
The factors influencing crop growth, development, and distribution have been widely discussed and studied for decades and have become a hot scientific issue [8,7,20,37].Crops, as a distinctive plant resource, bear a profound connection to both social and agricultural advancements [8,35].
Various determinants, encompassing climate, topography, hydrology, and soil composition, exert their influence on crop growth and development [7,21].Recent inquiries underscore that, amid these multifaceted influences, non-climatic factors predominantly govern short-term biological changes, with climate change emerging as the pivotal factor significantly impacting growth, development, and optimal distribution [6,21,33].Widely acknowledged as a critical driver of agricultural progress, climate change finds consensus within the scientific community [7,9,37].Research indicates that global warming substantially affects crop yields, posing a significant threat to global food security and sustainable development [2].Conducting a trend analysis reveals the imperative to steadfastly pursue basic food self-sufficiency as a strategic measure to uphold international peace and security [36].
Consequently, a systematic exploration into the geographical distribution patterns of crops and their responses to climate change in the context of global warming assumes paramount importance.This investigation not only furnishes scientific evidence crucial for production layout and planting management but also establishes a foundational framework for regional sustainable development, ecological stewardship, and environmental governance.The implications of this exploration extend significantly towards ensuring global food security in the face of evolving climatic conditions.Species Distribution Models (SDMs) have gained widespread use in examining the impact of climate change on the potential geographic range of species.These models play an increasingly pivotal role in forecasting species distribution by considering alterations in diverse environmental factors [11,21].Over ten distinct models, including Bioclim, Domain, GARP, MaxEnt, and others, have found applications across various domains such as plant conservation, utilization, and pest invasion [11,12,[28][29][30][31]41].Notably, the MaxEnt model possesses several advantages compared to its counterparts: (1) singular distribution records suffice during the modeling process, (2) it accommodates both continuous and discrete variables, (3) its operational simplicity eliminates the need for intricate format conversions of species distribution and environmental data, (4) a minimal sample of species "presence" data yields highly effective simulations, and (5) its theoretical underpinnings are closely aligned with ecological principles, enhancing understanding of species suitability [7,20,18,32].Given the divergence in theoretical foundations among various models, their simulation and prediction performances exhibit considerable variation.MaxEnt, among these models, consistently demonstrates superior simulation outcomes [10,33].Consequently, the MaxEnt model emerges as particularly well-suited for evaluating the potential global distribution of S. italica, considering its robust simulation results and alignment with the ecological context, as elucidated above.
The species S. italica, an annual herb belonging to the Gramineae family, possesses characteristics akin to millet, as depicted in Figure 1.It stands as one of the earliest globally domesticated crops, demonstrating resilience to drought, infertility, and high temperatures [16,44,25,42,43].Renowned for its nutritional value and enriched with natural active substances conferring hypoglycemic, hypolipidemic, and antioxidant properties, S. italica additionally serves as a noteworthy C4 biofuel source.This versatility is reflected in its processing into a myriad of food and industrial products, as illustrated in Figure 1d [4,44].As a paramount miscellaneous crop on the global stage, S. italica enjoys popularity due to its high yield and cost-effectiveness [1,44].Given the contemporary prevalence of high-protein dietary habits across a significant portion of the global population, the integration of S. italica into diets becomes instrumental in fostering diverse and balanced nutritional intake [44].In essence, the significance of S. italica extends beyond its economic contributions to encompass vital implications for maintaining human well-being and global food security.Despite its agricultural prominence, there exists a noteworthy gap in potential distribution studies for S. italica.
Ecological modeling studies with consideration of environmental variables, as demonstrated in several existing works [7,24,35,37], offer practical insights into predicting the potential suitable distribution of crops.However, the dearth of analogous studies for S. italica underscores the importance of research focused on delineating its current and future global suitable distribution.Such investigations hold promise not only for advancing agricultural development but also for addressing critical aspects of human life and health, thereby contributing meaningfully to resolving the ongoing global food crisis.

Sources of environmental variables
For our study, we gathered essential data from reputable sources to conduct a comprehensive analysis of the potential distribution of S. italica.Climate and digital elevation data for both current and future emission scenarios were retrieved from the WorldClim database (DEM, http://www.worldclim.org//).Soil data, integral to our investigation, corresponds to the Coordinated World Soil Database (HWSD V1.2, https://www.fao.org/),ensuring a robust understanding of the soil characteristics.UV-B radiation data, a crucial environmental factor, was sourced from the global UV-B radiation dataset (gIUV, https://www.ufz.de/gluv/index.php).
To enhance the spatial context of our study, we incorporated world administrative maps obtained from the National Science and Technology Infrastructure System Science Data Center China via National Earth (http://www.geodata.cn).The integration of these datasets was achieved through the utilization of ArcGIS software (version 10.2), facilitating a unified representation at a 5 arc-minute resolution.
Acknowledging the potential interdependence of environmental variables, we conducted a Spearman correlation analysis as depicted in Figure 4.This analysis allowed us to discern correlations among the variables before incorporating them into the MaxEnt model.In instances where the correlation coefficient between two environmental factors exceeded or equaled 0.8, a judicious approach was taken to retain the variable contributing more significantly.As a result of this selection process, eight key environmental variables were identified and utilized in subsequent MaxEnt model runs, ensuring the model's robustness and effectiveness in predicting the potential distribution of S. italica.

Fig. 4. Heat map for correlation analysis of environmental variables 2.3 Model construction and evaluation
The MaxEnt modeling process involved importing distribution point and environmental factor data for S. italica, which were divided into a test set (25%) and a training set (75%) to ensure the accuracy of our predictions.To address the inherent challenge of false positives and negatives in species distribution models, rigorous evaluation metrics were employed to assess the usability and accuracy of the model [20,33].Commonly utilized theoretical evaluation metrics, including overall accuracy, sensitivity, specificity, AUC values, TSS values, and Kappa statistics, were considered.For enhanced credibility, this study selected AUC values, TSS values, and Kappa statistics for model accuracy evaluation across three assessment methods.
In this study, we implemented trial models with different parameter settings to evaluate performance, optimizing these models based on the observed response to known distribution points of S. italica and their corresponding environmental factors.The adjustment process involved establishing the range of regularization multipliers (RM) from 0.5 to 4. Additionally, six feature combinations (FC) were employed to optimize the model parameters: L (linear features), LQ (linear features + quadratic features), H (hinge features), LQH (linear features + quadratic features + hinge features), LQHP (linear features + quadratic features + hinge features + product features), and LQHPT (linear features + quadratic features + hinge features + product features + threshold features).After careful evaluation, the optimal combination for the regularization multiplier and feature selection was determined to be RM and LQHPT, respectively.

Classification of potentially suitable areas
The MaxEnt model produced an ASCII raster layer depicting the probability (P) of S. italica presence, ranging from 0 to 1.To categorize these probabilities into habitat suitability classes, we followed the IPCC-CMIP6 guidelines [21,14]: A probability greater than 0.5 corresponds to a most suitable habitat.

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The range of 0.3 ≤ P < 0.5 designates a moderately suitable habitat.

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For probabilities within 0.1 ≤ P < 0.3, the habitat is considered low suitable.

Model accuracy evaluation
Based on the results (Fig. 5), we can conclude that the MaxEnt model predicted an "excellent" AUC value, and the Kappa and the TSS statistics indicated "good" performance; These evaluations confirm the model's appropriate configuration and high reliability for subsequent analysis.The potential suitable habitats for S. italica covered an expansive area of 5535.44×10 4 km 2 (Fig. 8), prominently located in the United States (North America), Brazil (South America), Australia (Oceania), China, India (Asia), and the Russian Federation (Europe) (Fig. 6).Within this distribution, high suitable habitats occupied 522.64×10 4 km 2 , constituting 9.44% of the total (Fig. 8).Notably, these high suitable habitats were concentrated in the United States, India, and the Russian Federation (Fig. 6).
Moderately suitable habitats extended over an area of 1313.75×10 4 km 2 , accounting for 23.73% of the total suitable areas (Fig. 8).These habitats were primarily observed in the Russian Federation, United States, China, and India (Fig. 6).Low suitable habitats comprised the largest portion, covering 3699.05×10 4 km 2 , representing 66.83% of the total suitable areas (Fig. 8).These areas were predominantly found in China, the United States, the Russian Federation, Brazil, Australia, and other countries (Fig. 6).In summary, the potential geographic range of S. italica, as predicted by the MaxEnt model (Fig. 6), surpassed its modern geographic range, illustrating the extensive potential distribution of the species.
Fig. 6.Current global geographical distributions of S. italica

Future potential geographical distributions of S. italica
Compared with the current climate conditions, the potentially suitable habitats of S. italica drastically reduced under four different emission scenarios (SSP1-2.6,SSP2-4.5,SSP3-7.0 and SSP5-8.5) in the 2050s and 2070s (Fig. 7).Under SSP3-7.0,we identified the maximum reduction at all levels of suitable habitats for both periods, followed by SSP2-4.Lastly, with the SSP5-8.5 scenario, a predicted reduction of 44.25% in total suitable habitats is anticipated in the 2050s and 43.60% in the 2070s.Highly suitable areas will again be most affected, with a decrease of 77.56% (2050s) and 76.74% (2070s).Medium suitable habitats will see a significant decline of 61.45% in the 2050s and 61.13% in the 2070s, while lower suitable habitats should be reduced by 33.44% (2050s) and 32.69% (2070s).
In summary, climate change is expected to significantly impact the suitable habitats of S. italica, leading to a loss of potential habitats in the coming decades under various emission scenarios.This transformation is characterized by the conversion of highly suitable areas into medium and low-suitable areas, as well as the shift of medium-suitable habitats towards low-suitable and unsuitable habitats (Figs. 7 and 8).In this study, we adopted a specific criterion for identifying dominant environmental factors.The top three environmental factors with the highest contribution rates were selected as dominant factors, as illustrated in Figure 9. Notably, across different periods and under various emission background concentrations, the contribution rates of soil factor, driest month precipitation (Bio14), and hottest season precipitation (Bio18) consistently ranked much higher than other variables, securing the top three positions.Consequently, these three factors were collectively utilized as the dominant data in this study, offering a focused and robust basis for the analysis.The assessment of relationships between the presence probability of S. italica and environmental factors was conducted through the analysis of response curves for each environmental variable.In this analysis, when the presence probability surpassed 0.5, it signified that the corresponding environmental factor value favored the growth of S. italica.To provide a more intuitive depiction of the influence of environmental variables on the distribution of S. italica, the response curve for the combination of dominant environmental variables in this study was derived, as illustrated in Figure 10.This graphical representation offers insights into how the probability of S. italica presence responds to variations in the combined values of the selected dominant environmental factors.The suitability of areas for S. italica exhibited a trend of both increasing and decreasing with rising precipitation.This trend may be attributed to precipitation levels exceeding the threshold suitable for the survival of S. italica in many regions, leading to a decrease in its probability of survival, especially in the SSP3-7.0emission scenario where temperature and precipitation values were outside the suitable ranges.This aligns with findings from Thomas et al., who studied the extinction risk of organisms on a sample area covering 20% of the Earth's surface, suggesting varying impacts on different species in response to climate change [26].While some species benefit from climate warming, the impact on S. italica is predominantly negative.
The suitable habitats of S. italica in the 2050s and 2070s show a contraction to the west in Europe, a trend to the north in Africa, and a trend towards the south in Asia, North America, South America, and Oceania.The SSP5-8.5 emission scenario in the 2070s indicates the smallest loss of suitable habitats for S. italica, covering an area of 2369.53 × 104 km2 (Fig. 10).Conversely, the largest loss of suitable habitat occurs in the 2050s under the SSP3-7.0emission scenario, with an area of 3576.80 × 104 km2 (Fig. 10).China, the United States, Brazil, and Australia are highlighted as regions experiencing the most significant loss.
Under the SSP5-8.5 emission scenario, S. italica is projected to experience the least reduction in suitable habitats at all levels.This resilience is attributed to the species' ability to tolerate high temperatures and drought, making it less vulnerable to this particular emission scenario.These results align with other studies examining the response of various species to changing climate conditions.both low and excessive [40,34].The study corroborates findings by Zhang, emphasizing the substantial impact of moisture conditions on S. italica yield, projecting a significant influence on the species' probability of existence with future precipitation increments [39].
Cao's study, based on actual weather station data, supports the conclusion that climatic variables such as driest monthly precipitation and hottest seasonal precipitation play a primary role in constraining the growth and distribution of S. italica [22].These results are consistent with the findings of this study.Further, the influence of soil factors on S. italica growth and development is supported by research conducted by Kaur, Bandyopadhyay, and Nissi, highlighting the significance of soil fertility and microorganisms in soil as important environmental factors for S. italica [13,3,23].
Historical studies by Dong and Chen, exploring the development and decline of S. italica cultivation over millennia, underscore the importance of temperature as a significant constraint on the potential geographical distribution of S. italica [9,8].These findings align with the temperature-related factors identified in the MaxEnt model predictions.
The study focuses on predicting the potential geographic distribution of S. italica in China and identifies the environmental variables limiting its potential distribution.Changes in the study area over time may impact the range of environmental factors influencing S. italica growth.While other environmental factors, such as vegetation cover, could influence the species' potential distribution, they were not included in the study due to challenges in accurately predicting global vegetation cover in the future.Therefore, the results obtained should be applied in practice with consideration for local hydrogeological conditions.Overall, the study contributes valuable insights to the global macro plan for the rational cultivation of S. italica.

The importance of conducting simulations of the potential geographic distribution of S. italica
Global climate change poses a serious threat to agricultural production, directly impacting food security and, consequently, human development.The food crisis, stemming from climate change, has a far-reaching impact, making it one of the most critical challenges globally [2,37].The ecological niche models used in this study to project the potential geographic distribution of S. italica contribute to a clearer understanding of how this crop responds to climate change.As a significant global miscellaneous crop, S. italica plays a crucial role in ensuring food security and human health.
The study's predicted results indicate that the potential geographic distribution of S. italica is adversely affected in the context of climate change.These findings serve as a crucial first step in the macro plan for the rational cultivation of S. italica.To address these challenges, promoting the planting of S. italica in highly suitable areas, especially in the context of grassland restoration, is recommended.
Increasing knowledge and implementing practices for controlling pests and diseases affecting S. italica are essential.Immediate actions, such as quarantine measures for affected crops and treatment of diseases and insect infestations, are crucial.Through the application of scientific knowledge and effective control measures, it is possible to mitigate the impact of climate change on S. italica cultivation, contributing to broader food security efforts.

Conclusions
The simulation of the potential geographical distribution of S. italica based on current climate conditions indicates its main distribution areas include the United States (North America), Brazil (South America), Australia (Oceania), China, India (Asia), and the Russian Federation (Europe).
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Fig. 1 .
Fig. 1.Stages of S. italica Processing: (a) Unprocessed, (b) Harvesting, (c) After shelling, (d) Processed products.Given the absence of dedicated studies on the potential geographical distribution of S. italica, employing the MaxEnt model emerges as a suitable approach.While numerous studies have applied various models to predict crop distributions, the specific considerations for S. italica have been notably absent.The cultivation of S. italica currently confronts practical challenges, including the unknown potential planting areas and the imperative to comprehend its distribution dynamics in response to climate change within the context of global warming.This study aims to address these challenges by predicting the potential distribution of S. italica across different periods, offering a theoretical reference for cultivation practices.The research endeavors to unveil the present and future distribution patterns of S. italica, delineate trends in its potential distribution under the influence of global warming and put forth development strategies to address emerging challenges.Furthermore, the study explores the intricate relationship between the distribution of S. italica and climate change, as illustrated in Figure 2. By delving into these aspects, the research aims to contribute valuable insights for the theoretical underpinning of S. italica cultivation, facilitating informed decision-making in the face of uncertainties associated with global warming.

Fig. 2 .
Fig. 2. Flowchart displaying the steps of the present study

Fig. 5 .
Fig. 5. AUC, Kappa and TSS values of MaxEnt 3.2 Current potential geographical distributions of S. italica

Fig. 8 .
Fig. 8. Suitable areas for S. italica under different climate change scenarios(10 4 km 2 )3.4 Combinations of dominant environmental variables affecting the distribution of S. italicaThe determination of dominant factors in environmental studies is marked by varying perspectives among researchers.Presently, a prevalent approach involves establishing dominance based on the contribution rate of environmental factors.This entails considering factors as dominant once they surpass a certain contribution rate threshold.However, the diversity in criteria arises due to the subjective nature of threshold selection.

Fig. 10 .
Fig. 10.Response curves of existence probability of S. italica 4. Discussion 4.1 Changes in the potential geographic distribution of S. italica under future climate change scenarios In this study, the MaxEnt model was employed to predict changes in the potential global geographic distribution of S. italica, utilizing environmental variables from both current and future climate scenarios under four different emission scenarios (Fig. 10).The model projections indicate that the potential geographic distribution of S. italica in the 2050s and 2070s under the four emission scenarios is expected to be lower compared to the potential distribution under modern climate conditions.

It's important
to note that climate change could indirectly affect the population and distribution characteristics of S. italica by influencing the ecosystem directly.Additionally, irrational human activities, such as urban construction, hydroelectric development, and other industrial practices, could further contribute to a dramatic decline in potential distribution areas.The study's focus on two time periods, 2050 and 2070, for environmental factor variables suggests the potential for future research to consider multiple periods to derive an overall trend in the species' potential geographic distribution in response to climate changes.

Fig. 11 .
Fig. 11.Changes in the potential geographical distribution of S. italica under climate change scenarios in the future 4.2 Constraints of environmental variables on the potential geographical distribution of S. italica The MaxEnt model results highlight soil and precipitation factors, specifically the driest monthly precipitation and hottest seasonal precipitation, as critical environmental factors limiting the potential geographic distribution of S. italica.The study reveals that the probability of S. italica presence is influenced by variations in these precipitation factors, with increased driest-month precipitation and hottest-season precipitation positively impacting the probability of existence.This aligns with previous research by Zhang and Yang, demonstrating the sensitivity of S. italica growth to precipitation levels, However, under future climate change scenarios, there is a significant reduction in the potential distribution areas of S. italica.This reduction could have unfavorable implications for the future cultivation of S. italica.The critical environmental factors identified as limiting the potential geographical distribution of S. italica include soil and precipitation factors, specifically the driest monthly precipitation and hottest seasonal precipitation.In conclusion, the study suggests that climate change has the potential to significantly impact the future distribution pattern of S. italica cultivation, leading to a reshaping of production and trade patterns.This insight is valuable for anticipating and addressing challenges in S. italica cultivation in the face of changing climate conditions.FundingThe Scientific research initiation project of Mianyang Normal University (QD2019A13, QD2021A37 and QD2023A01), the Funding of the Open Project from the Ecological Security and Protection Key Laboratory of Sichuan Province (ESP1608, ESP2008, ESP2201 and ESP2204), the Sichuan Provincial Education Department Scientific Research Project (15ZB0283), the Sichuan Provincial Science and Technology Department Project (2023NSFSC0750).
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